constants.py

APP_TITLE = "📐 NER Metrics Comparison ⚖️"

APP_INTRO = "The NER task is performed over a piece of text and involves recognition of entities belonging to a desired entity set and classifying them. The various metrics are explained in the explanation tab. Once you go through them, head to the comparision tab to test out some examples."


### EXPLANATION TAB ###

EVAL_FUNCTION_INTRO = "An evaluation function tells us how well a model is performing. The basic working of any evaluation function involves comparing the model's output with the ground truth to give a score of correctness."
EVAL_FUNCTION_PROPERTIES = """
Some basic properties of an evaluation function are -
1. Give an output score equivalent to the upper bound when the prediction is completely correct(in some tasks, multiple variations of a predictions can be considered correct)
2. Give an output score equivalent to the lower bound when the prediction is completely wrong.
3. GIve an output score between upper and lower bound in other cases, corresponding to the degree of correctness.
"""
NER_TASK_EXPLAINER = """
The output of the NER task can be represented in either token format or span format.
"""
### COMPARISION TAB ###

PREDICTION_ADDITION_INSTRUCTION = """
Add predictions to the list of predictions on which the evaluation metric will be caculated.
- Select the entity type/label name and then highlight the span in the text below.
- To remove a span, double click on the higlighted text.
- Once you have your desired prediction, click on the 'Add' button.(The prediction created is shown in a json below)
"""